This promoted content is produced by a publishing partner of Open Mic. A paid-for membership product for partners of The Drum to self-publish their news, opinions and insights on thedrum.com - Find out more
Computer vision and DAM: improve accuracy, reduce costs, what’s not to love?
August 23, 2021
When you combine computer vision and digital asset management (DAM) you can improve content accuracy, reduce costs, and increase return on content investments. Imagine your whole library of images (and videos, text, audio, etc.) being auto tagged and organised with accurate metadata by out-of-the-box artificial intelligence (AI) technology. It’s not a future you have to wait for either, it’s available now.
Computer vision combined with DAM can help your product managers build new customer experiences quickly, improve creative workflows, and give your marketing team new product search and recommendation capabilities. With deep learning added to the computer vision AI models, you can get the full benefits of metadata generation, auto tagging, and visual search. So what exactly is computer vision? Let’s start by defining it.
What is computer vision?
Computer vision is a subset of AI technology that analyses images. It lets you use pre-trained AI models out of the box to search, sort, and organise unstructured data. That means you can use computer vision to auto tag and organise any size library of digital assets by specific image attributes without a manual process.
Then you can deep train these computer-vision models to detect faces, explicit content types, embedded objects, and text within images. This gives your AI technology the ability to predict various attributes such as celebrities, food items, textures, colours, and more. With these deep learning capabilities you could build everything from product searches based on colour to facial recognition security features and automated content moderation tools.
How does deep learning work?
Deep learning is when you set your computer vision models up to learn from your data without human supervision. Let’s take deep metadata generation for example. When you use a platform like Clarifai for e-commerce, you start with a general AI model that can recognise and generate image metadata from over 11,000 different concepts. It applies that model to your unstructured image library to auto generate and assign metadata, learning from your brand’s unique content.
Some other direct applications of deep learning that are useful to marketing and creative teams include deep visual search and content moderation. Visual search helps marketing teams improve their product recommendations, produce better search results, and personalise the customer shopping experience. Automated deep content moderation can also protect your brand from damaging user-generated content (UGC). You can train your AI models to recognise unwanted content and keep it from being uploaded in the first place.
Computer vision and martech
Computer vision and deep learning can be combined with multiple marketing technologies for different benefits and results. For example, when you enhance your product information management (PIM) platform with deep learning you can create more personalised product recommendations for customers.
When combined with your DAM system, computer vision can help your users find images without trying to describe them with words. You can search by image to find similar assets that support the mood of a marketing campaign or find similar images that resonate with popular social media posts.
One of the biggest benefits of combining computer vision with DAM is that you get accurate and complete metadata without wasting time on manual entry. Here are some of the ways your company and specific roles can benefit from computer vision.
Who benefits from computer vision?
A company in any industry who relies on images and videos as key strategic assets can benefit from using computer vision. Brands in media and entertainment, manufacturing, and e-commerce can all use this AI technology to automate and scale their content operations.
If your company has product marketing managers, they can use computer vision to fuse structured and unstructured data to build more robust search features and personalised product recommendations.
Your content production manager can use deep metadata generation to eliminate manual content processes and save costs. Social media and community directors can use deep content moderation to protect your brand’s integrity. And your director of digital marketing can use deep media similarity to cluster, de-duplicate, rank, and geo-locate your media for advanced marketing targeting.
What are the benefits of computer vision technology?
Computer vision benefits both customers and brands. Customers get more robust search features, better product recommendations, and the ability to search for products from images they snapped with their phones. Brands get all the business benefits that come with happier customers along with improved workflows, reduced costs, and less time wasted on manual content organisation.
Benefits of auto tagging
Using AI-powered automated metadata generation comes with a lot of perks. It helps you improve catalogue descriptions and asset searchability. It gives you the ability to build intelligent searches based on objects, people, colours, emotions, and demographic characteristics. You also can build and train custom AI models to run custom automated workflows to realise the full benefits of auto tagging.
Other auto tagging benefits include:
- Generating metadata 100x faster than manual processing
- Reducing tagging errors by up to 80%
- Providing more meaningful product descriptions and information your customers can consistently rely on
- Improving product visibility
- Tagging data in 53 languages
- Reducing costs — using auto tagging is 90% less expensive than manual tagging
Benefits of visual search
When it comes to sales, the easier a product is to find, the easier it is to buy. That’s why e-commerce companies often depend on rich search to improve discoverability and provide shopping experiences that convert. Leveraging visual search can also allow your customers to use a photo from their phone to find a product in your catalogue. And since AI captures insights from your images, you can use that information to build personalised customer experiences through search and visual product associations.
Other visual search benefits include:
- An estimated 15% increase in customer revenue through product personalisation
- Ability to shorten the path from search to conversion
- Opportunity to increase cart size by suggesting hyper-personalised related products
- Reduction of bounce rates by delivering faster, more relevant search results
- Up to 2x faster checkout times using visual search over text-based search
Computer vision and AI technology use cases
Companies are using computer vision for everything from deep quality control in manufacturing lines to deep metadata generation for digital asset libraries. Here’s a quick look at some of the common e-commerce use cases that marketing and creative teams build with this AI technology.
Product categorisation and search
When you have a large online catalogue, it’s vital that you improve your product category search results with richer AI-automated metadata. Over time, you’ll end up with more descriptive and meaningful keywords than humans can come up with. You can even classify and segment these product details to create searchable items and help customers locate products more quickly.
Shop by look or theme
Have you ever created a marketing campaign based on a collection of products or themes? Now you can drive shopping cart size by helping customers find similar products they may not search for on their own. Your look or theme could be a room (e.g. kitchen, living room, bathroom), a certain style or outfit, or an activity (e.g. skiing, mountain biking, car camping). AI technology lets you create collections that cater to unique preferences and give people a more personalised experience.
Snap and search
Have you ever taken a picture of something you love and want to buy? Sometimes customers love a certain style but they don’t know how to describe it in a keyword search. Computer vision lets your audience find exactly what they're looking for by using a photo taken from their mobile phone. With snap and search they can match their photos to similar or exact items in your product catalogue and order what they want.
Computer vision and DAM
Computer vision is a natural fit to support your digital asset management efforts. Starting with auto tagging and metadata generation, you can save costs, eliminate manual processes, and increase content accuracy. Imagine having accurate and complete metadata values for every digital asset in your system. With computer vision and DAM, you can let AI take the first pass and make all your assets instantly searchable.
Combining this AI technology can help you generate metadata up to 100x faster than manual processing and reduce tagging errors by 80%. That ultimately leads to improved product visibility, shortened paths from search to conversion, and increased cart sizes from personalised product recommendations.
Product marketing managers, content managers, and digital marketing managers can all combine computer vision with DAM to help hit their goals and bring their roadmaps to life faster. Any organisation can add computer vision to their DAM system to increase content data accuracy, protect their brand, and improve digital customer experiences.
Our award-winning DAM platform, the Widen Collective®, gives teams the latest DAM and computer vision technology in one intelligent package. We partner with Clarifai — known for their extremely fast visual search capabilities and custom model support — to provide Collective customers with everything they need to scale their content operations using AI.
Find out how DAM and computer vision combine to help teams automate the management of thousands or millions of digital assets.
By Nate Holmes, product marketing manager, Widen